{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OpenDataTools 支持黑色系现货数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入spot接口\n",
    "from opendatatools import spot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据接口"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>indicator_id</th>\n",
       "      <th>indicator_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>钢材指数</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61</td>\n",
       "      <td>铁矿指数</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64</td>\n",
       "      <td>焦炭指数</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1002</td>\n",
       "      <td>煤炭指数</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1003</td>\n",
       "      <td>水泥指数</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1100</td>\n",
       "      <td>FTZ指数</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>118</td>\n",
       "      <td>钢铁行业PMI指数</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>119</td>\n",
       "      <td>钢铁行业PMI生产指数</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120</td>\n",
       "      <td>钢铁行业PMI新订单指数</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>121</td>\n",
       "      <td>钢铁行业PMI新出口订单指数</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>122</td>\n",
       "      <td>钢铁行业PMI产成品库存指数</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>123</td>\n",
       "      <td>钢铁行业PMI原材料库存指数</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>74</td>\n",
       "      <td>沪市终端线螺每周采购量监控</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>72</td>\n",
       "      <td>沪螺纹钢社会库存</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>67</td>\n",
       "      <td>国内螺纹钢社会库存量</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>68</td>\n",
       "      <td>国内线材社会库存量</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>69</td>\n",
       "      <td>国内主要城市热轧卷板库存</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>70</td>\n",
       "      <td>国内主要城市冷轧卷板库存</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>73</td>\n",
       "      <td>国内主要城市中厚板库存</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>117</td>\n",
       "      <td>全国主要钢材品种库存总量</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   indicator_id  indicator_name\n",
       "0            65            钢材指数\n",
       "1            61            铁矿指数\n",
       "2            64            焦炭指数\n",
       "3          1002            煤炭指数\n",
       "4          1003            水泥指数\n",
       "5          1100           FTZ指数\n",
       "6           118       钢铁行业PMI指数\n",
       "7           119     钢铁行业PMI生产指数\n",
       "8           120    钢铁行业PMI新订单指数\n",
       "9           121  钢铁行业PMI新出口订单指数\n",
       "10          122  钢铁行业PMI产成品库存指数\n",
       "11          123  钢铁行业PMI原材料库存指数\n",
       "12           74   沪市终端线螺每周采购量监控\n",
       "13           72        沪螺纹钢社会库存\n",
       "14           67      国内螺纹钢社会库存量\n",
       "15           68       国内线材社会库存量\n",
       "16           69    国内主要城市热轧卷板库存\n",
       "17           70    国内主要城市冷轧卷板库存\n",
       "18           73     国内主要城市中厚板库存\n",
       "19          117    全国主要钢材品种库存总量"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取现货指标\n",
    "df_indicator = spot.get_commodity_spot_indicator()\n",
    "df_indicator.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>change</th>\n",
       "      <th>chg_pct</th>\n",
       "      <th>date</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>2018-06-21</td>\n",
       "      <td>640.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>2018-06-20</td>\n",
       "      <td>640.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>2018-06-19</td>\n",
       "      <td>640.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-5.00</td>\n",
       "      <td>-0.78%</td>\n",
       "      <td>2018-06-15</td>\n",
       "      <td>640.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>2018-06-14</td>\n",
       "      <td>645.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>2018-06-13</td>\n",
       "      <td>645.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>2018-06-12</td>\n",
       "      <td>645.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>2018-06-11</td>\n",
       "      <td>645.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>2018-06-08</td>\n",
       "      <td>645.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>2018-06-07</td>\n",
       "      <td>645.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>2018-06-06</td>\n",
       "      <td>645.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>2018-06-05</td>\n",
       "      <td>645.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>2018-06-04</td>\n",
       "      <td>645.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>645.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>2018-05-31</td>\n",
       "      <td>645.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>2018-05-30</td>\n",
       "      <td>645.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>2018-05-29</td>\n",
       "      <td>645.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>2018-05-28</td>\n",
       "      <td>645.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>645.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>2018-05-24</td>\n",
       "      <td>645.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   change chg_pct        date   value\n",
       "0    0.00   0.00%  2018-06-21  640.00\n",
       "1    0.00   0.00%  2018-06-20  640.00\n",
       "2    0.00   0.00%  2018-06-19  640.00\n",
       "3   -5.00  -0.78%  2018-06-15  640.00\n",
       "4    0.00   0.00%  2018-06-14  645.00\n",
       "5    0.00   0.00%  2018-06-13  645.00\n",
       "6    0.00   0.00%  2018-06-12  645.00\n",
       "7    0.00   0.00%  2018-06-11  645.00\n",
       "8    0.00   0.00%  2018-06-08  645.00\n",
       "9    0.00   0.00%  2018-06-07  645.00\n",
       "10   0.00   0.00%  2018-06-06  645.00\n",
       "11   0.00   0.00%  2018-06-05  645.00\n",
       "12   0.00   0.00%  2018-06-04  645.00\n",
       "13   0.00   0.00%  2018-06-01  645.00\n",
       "14   0.00   0.00%  2018-05-31  645.00\n",
       "15   0.00   0.00%  2018-05-30  645.00\n",
       "16   0.00   0.00%  2018-05-29  645.00\n",
       "17   0.00   0.00%  2018-05-28  645.00\n",
       "18   0.00   0.00%  2018-05-25  645.00\n",
       "19   0.00   0.00%  2018-05-24  645.00"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取现货数据\n",
    "# 例如：铁矿指数\n",
    "df, msg = spot.get_commodity_spot_indicator_data('61')\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "df['date'] = df['date'].apply(lambda x : datetime.datetime.strptime(x, '%Y-%m-%d'))\n",
    "df['value'] = df['value'].apply(lambda x : float(x))\n",
    "df.set_index('date', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_indicator(id):\n",
    "    \n",
    "    from matplotlib.pylab import mpl\n",
    "    # 指定默认字体\n",
    "    mpl.rcParams['font.sans-serif'] = ['FangSong']\n",
    "    # 解决保存图像是负号'-'显示为方块的问题\n",
    "    mpl.rcParams['axes.unicode_minus'] = False\n",
    "    \n",
    "    # 获取现货数据\n",
    "    df, msg = spot.get_commodity_spot_indicator_data(id)\n",
    "    import datetime\n",
    "    df['date'] = df['date'].apply(lambda x : datetime.datetime.strptime(x, '%Y-%m-%d'))\n",
    "    df['value'] = df['value'].apply(lambda x : float(x))\n",
    "    df.set_index('date', inplace=True)\n",
    "    \n",
    "    df_indicator = spot.get_commodity_spot_indicator()\n",
    "    df_indicator.set_index('indicator_id', inplace=True)\n",
    "    name = df_indicator.loc[id]['indicator_name']\n",
    "    \n",
    "    # 画图\n",
    "    import matplotlib.pyplot as plt\n",
    "    plt.figure(figsize=(16,8))\n",
    "    plt.plot(df.index, df['value'])\n",
    "    plt.title(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x161758d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_indicator('1002')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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